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[CIDC FTP Data]
[GPCP PCP IDC Data on FTP]
Data Access
GPCP Global Combined Precipitation Data
[rule]
Readme Contents
Data Set Overview
Sponsor
Original Archive
Future Updates
The Data
Characteristics
Source
The Files
Format
Name and Directory Information
Companion Software
The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data
Contacts
Points of Contact
References
[rule]
Data Set Overview
This global precipitation dataset is a merged analysis
incorporating precipitation estimates from low-orbit-satellite
microwave data, geosynchronous-orbit satellite infrared data, and
rain gauge observations. The dataset is comprised of monthly
gridded area-mean rainfall totals and error estimates, for the
period covering July 1987 to December 1997. For consistency with
the other datasets in the Goddard DAAC's Climate Interdisciplinary
Data Collection (CIDC), the original 2.5x2.5 degree gridded
precipitation data received from NOAA National Climate Data Center
is regridded to 1x1 degree grid. The original dataset is formally
referred to as the "GPCP Version 1a Combined Precipitation Data
Set", which is often abbreviated to "GPCP Combined Data Set" or
"Version 1a Data Set". It has been produced for the Global
Precipitation Climatology Project(GPCP), an international effort
organized by GEWEX/WCRP/WMO to provide an improved long-term
precipitation record over the globe(for details see WMO ,1985; and
WMO/ICSU,1990) with the purpose of evaluating and providing global
gridded data sets of monthly precipitation based on all suitable
observation techniques as a basis for:
* verification of climate model simulations
* investigations of the global hydrological cycle
* climate change detection studies
Sponsor
The production and distribution of this data set are being funded
by NASA's Earth Science enterprise. The data are not copyrighted;
however, we request that when you publish data or results using
these data please acknowledge as follows:
The authors wish to thank the Distributed Active Archive
Center (Code 902) at the Goddard Space Flight Center,
Greenbelt, MD, 20771, for producing the data in its
present format and distributing them. The original data
products were produced by the science investigators Dr.
George Huffman and Dr. Robert Adler of Laboratory of
Atmospheres, Code 912, NASA Goddard Space Flight Center,
Greenbelt, Maryland 20771 USA, as the Global
Precipitation Climatology Project (GPCP) Merge
Development Centre, and is archived at World Data Center
A (WDC-A) for Meteorology at the National Climate Data
Center (NCDC) in Asheville, North Carolina. Goddard
DAAC's share in these activities was sponsored by NASA's
Earth Science enterprise.
Original Archive
The original combined version 1a precipitation data along with
intermediate products (on 2.5 x 2.5 degree grid) and detailed
document is currently available from the archive WDC-A, NOAA
National Climatic Data Center (NCDC) . The original dataset
including the precipitation estimates from individual input fields
(microwave and infrared satellite estimates, their combinations,
and rain gauge analysis) is also available through our Hydrology
Data collection site. The anonymous FTP site for GPCP v1a Combined
Precipitation is
ftp://daac.gsfc.nasa.gov/data/hydrology/precip/gpcp/gpcp_v1a_combined.
Future Updates
The Goddard DAAC will update this data set as new data are
processed and made available at NCDC.
The Data
Characteristics
* Parameters:
Surface Precipitation
Error Estimates
* Units: mm/day
* Typical Range: 0-50 for precipitation values; and 0-15 for
error estimates
* Temporal Coverage: July 1987 - December 1997 (except December
1987) Start and gap in temporal coverage are based on the
availability of SSM/I component of multi-satellite data; End
is based on the availability of the rain gauge analyses
* Temporal Resolution: Monthly Means in units of mm/day
* Spatial Coverage: Global
* Spatial Resolution: 1 degree x 1 degree
Source
This GPCP global combined precipitation data on 1x1 degree grid is
derived from the original GPCP Version 1a Combined Precipitation
Data Set (Huffman,1997b) which contains the final product
satellite and rain gauge merged precipitation estimates as well as
the intermediate products (the individual input fields such as
Infrared Geosynchronous Precipitation Index (GPI), Special Sensor
Microwave/Imager (SSM/I), and rain gauge estimates, their
combinations, and error estimates) as supporting information on a
2.5 degree by 2.5 degree grid for the period July 1987 to June
1997.
The input fields for producing the GPCP version 1a combined
product has been provided to the GPCP Merge Developement Center
(Huffman et al., 1995) by following GPCP participating
institutions:
GPCP Polar Satellite Precipitation Data Center (SSM/I
emission estimates)
NOAA Office of Research and Application (SSM/I scattering
estimates)
GPCP Geostationary Satellite Precipitation Data Center (GPI
estimates)
GPCP Global Precipitation Climatology Centre (rain gauge
analyses)
These individual data sets, as well as the combinations based on
them are contained in the original version 1a data set.
The Files
The global combined precipitation data set contains global gridded
rainfall estimates. There are two files for each month of the
data. One file is the the satellite and gauge merged precipitation
estimates and the other file contains the error estimates in the
precipitation for that month. Data in each file progresses from
North to South and from West to East beginning at 180 degrees West
and 90 degrees North. Thus first point represents the grid cell
centered at 89.5 degree North and 179.5 West. Grids with missing
values are filled with missing value code ( -99.99).
Format
Data Files
* File Size: 259200 bytes, 64800 data values
* Data Format: IEEE floating point notation
* Headers, trailers, and delimiters: none
* Missing Code: -99.99
* Image orientation: North to South
Start position: (179.5W, 89.5N)
End position: (179.5E, 89.5S)
Name and Directory Information
Naming Convention:
The file naming convention for the GPCP Global Combined
Precipitation Dataset is
gpcp_v1a.psg.1nmegg.[yymm].ddd (precipitation values)
gpcp_v1a.esg.1nmegg.[yymm].ddd (error estimates)
where:
gpcp_v1a = data product designator
psg(or esg) = parameter name: precipitation(or
error)satellite-gauge
1 = number of levels
n = vertical coordinate, n= not applicable
m = temporal period, m = monthly
e = horizontal grid resolution, e = 1 x 1 degree
go = spatial coverage, gg = global (land & ocean)
yy = year
mm = month
ddd = file type designation, (bin=binary, ctl=GrADS control
file)
Directory Path to Data Files
/data/inter_disc/hydrology/precip/gpcp/gpcp_v1a_cmb/yyyy/
where yyyy refers to year.
Companion Software
Several software packages have been made available on the CIDC
CD-ROM set. The Grid Analysis and Display System (GrADS) is an
interactive desktop tool that is currently in use worldwide for
the analysis and display of earth science data. GrADS meta-data
files (.ctl) have been supplied for each of the data sets. A GrADS
gui interface has been created for use with the CIDC data. See the
GrADS document for information on how to use the gui interface.
Decompression software for PC and Macintosh platforms have been
supplied for datasets which are compressed on the CIDC CD-ROM set.
For additional information on the decompression software see the
aareadme file in the directory:
software/decompression/
Sample programs in FORTRAN, C and IDL languages have also been
made available to read these data. You may also acquire this
software by accessing the software/read_cidc_sftwr directory on
each of the CIDC CD-ROMs
The Science
Theoretical Basis of Data
Knowledge of the spatial and temporal distribution of large scale
precipitation is required in the study of climate change. Spatial
distribution of the precipitation identifies the regions of
maximum latent heat release which is a major driving force of the
atmospheric circulation. The precipitation estimates are available
from different satellite and surface observations. However, each
source has strengths and weaknesses. The geostationary infrared
observations provide good temporal resolution and diurnal coverage
of precipitation systems. However, the relation between infrared
radiance and instantaneous surface precipitation is relatively
weak and useful primarily for deep convective systems in the 40
deg N-S latitude zone. The SSM/I microwave radiances have strong
connection with surface rainfall, especially over the ocean, and
are useful to much higher latitudes. However, the SSM/I
observations have poor temporal sampling. Surface rain-gauge
measurements are accurate but mostly limited to land areas.
Recognizing such shortcomings, World Climate Research Programme
(WCRP 1986) initiated the Global Precipitation Climatology Project
(GPCP) with the goal of the production of an improved long-record
estimates of precipitation over the globe from the blend of the
various satellite and surface precipitation estimates(Huffman et
al. 1997).
The GPCP sponsored several Algorithm Intercomparison Projects
(referred to as AIP-1, AIP-2, and AIP-3) for the purpose of
evaluating and intercomparing a variety of satellite precipitation
estimation techniques. As well, the NASA Wetnet Project has
sponsored several such projects (referred to as Precipitation
Intercomparison Projects, and labeled PIP-1, PIP-2, and PIP-3).
One use of these projects has been to identify competitive
techniques for use in the GPCP combined data set. Various groups
in the international science community are given the tasks of
preparing precipitation estimates from individual data sources,
then the GPCP Merge Development Centre (GMDC), located at NASA
Goddard Space Flight Center in the Laboratory for Atmospheres is
charged with combining these into a "best" global product. The
satellite-gauge precipitation product of the GPCP Version 1a
Combined Precipitation Data Set is the "final" blended
precipitation estimate produced by an algorithm developed by
Huffman et al.(1995) at GMDC, NASA/GSFC. Only a few similar data
sets are available. The earlier combined precipitation data set
produced by the GPCC is superseded by the Version 1a Data Set,
produced at NASA/GSFC. The combination data set by Xie and Arkin
(1996) uses similar input data and has similar temporal and
spatial coverage, but is carried out with a much different
technique.
Processing Sequence and Algorithms
The algorithm used by Huffman et al.(1995) at GMDC, NASA/GSFC, for
estimating the area-average precipitation first produces a
multi-satellite precipitation product based on a merged analysis
using all available satellite estimates and then finally combining
the multi-satellite analysis with rain-gauge analysis.
In the first step preliminary combinations and adjustments are
made. Microwave measurements are used to adjust the IR based GPI
and form the Adjusted Geosynchronous Precipitation Index (AGPI) in
the latitude belt 40 deg N-S. The geosynchronous meteorological
satellites give three hourly temporal coverage but their sensors
detect high cold clouds which are normally associated with rain
storms. The association with rain amounts is statistical and is
only reasonably accurate at mid and low latitudes ((Arkin and
Meisner,1987; Arkin et al. 1994). In addition geosynchronous
measurements are not available for all longitudes at all times.
Microwave sensors on lower sun synchronous satellites detect the
rain directly although determining the rain amounts is still a
difficult art. In the GPCP Version 1a Combined product two
microwave rain algorithms are used, one for ocean regions based on
emission (Chang et al., 1995; Wilheit et al., 1991)) and one for
land based on scattering ( Ferraro, et al., 1994; Weng and Grody,
1994, Grody 1991). The microwave measurements cover the entire
globe but at a lower temporal and spatial resolution. The AGPI has
the (usually low) bias of the microwave measurements together with
the smoothness and temporal coverage of the geosynchronous IR
measurements. In addition a microwave composite precipitation
product is formed by combining the SSM/I emission estimate over
water and the SSM/I scattering estimate over land. Since the
emission technique eliminates land-contaminated pixels
individually, a weighted transition between the two results is
computed in the coastal zone.
In the second step, the various satellite data sets are merged to
produce a best global satellite estimate. AGPI estimates are taken
where available in the latitudes 40 deg N-S belt. Where these are
missing in this belt, a weighted combination of the SSM/I
composite estimate and the microwave-adjusted low-orbit IR are
inserted. The combination weights are the inverse (estimated)
error variances of the respective estimates. Such weighted
combination of microwave and microwave-adjusted low-orbit IR is
done because the low-orbit IR lacks the sampling to warrant the
AGPI adjustment scheme. At higher latitudes the SSM/I composite
values are used since IR estimates become less accurate at high
latitudes.
The rain gauge precipitation product is produced by the Global
Precipitation Climatology Centre (GPCC) under the direction of B.
Rudolf, located in the Deutscher Wetterdienst, Offenbach A.M.,
Germany (Rudolf 1993,1996). Rain gauge reports are archived from
about 6700 stations around the globe, both from Global
Telecommunications Network reports, and from other regional or
national data collections. An extensive quality-control system is
run, featuring an automated step and then a manual step designed
to retain legitimate extreme events that typify precipitation. A
variant of the SPHEREMAP spatial interpolation routine (Willmott
et al. 1985) is used to analyze station values to area averages.
The analyzed values have been corrected for systematic error
following Legates (1987).
The final product is blend of multi-satellite and rain-gauge
estimates.
Final Combined Multi-Satellite and Gauge Precipitation Product:
The satellite-gauge precipitation product is produced as part of
the GPCP Version 1a Combined Precipitation Data Set by the GPCP
Merge Development Center in two steps (Huffman et al. 1995).
First, the multi-satellite estimate is adjusted toward the
large-scale gauge average for each grid box over land. That is,
the multi-satellite value is multiplied by the ratio of the
large-scale (5x5 grid-box) average gauge analysis to the
large-scale average of the multi-satellite estimate.
Alternatively, in low-precipitation areas the difference in the
large-scale averages is added to the multi-satellite value but
only when the averaged gauge exceeds the averaged multi-satellite.
In the second step, the gauge-adjusted multi-satellite estimate
and the gauge analysis are combined in a weighted average, where
the weights are the inverse (estimated) error variance of the
respective estimates.
Missing Value Estimation:
There is generally no effort to "estimate missing values" in the
single-source data sets, although a few missing days of gauge data
are tolerated in computing monthly values.
However, two cases of missing data are considered while computing
the "AGPI coefficients". First, when SSM/I data are missing in a
region, but GPI data exist, the coefficients are smoothly filled
across the blank. Second, when low-orbit IR data are used to fill
holes in the geosynchronous-orbit IR data, the low-orbit IR data
are used to estimate a smoothed AGPI. Specifically, the ratio of
the AGPI and the GPI computed from low-orbit IR data is computed
around the edge of the hole, the ratio is smoothly filled across
the hole, and the ratio is multiplied by the low-orbit GPI at each
point in the hole.
Error Estimation:
The "absolute error variable" is produced as part of the GPCP
Version 1a Combined Precipitation Data Set by the GPCP Merge
Development Center. Following Huffman (1997a), bias error is
neglected compared to random error (both physical and
algorithmic), then simple theoretical and practical considerations
lead to the functional form
H * ( rbar + S) * [ 720 + 268 * SQRT ( rbar ) ]
VAR = ----------------------------------------------- (1)
Ni
for absolute error, where VAR is the estimated error variance of
an average over a finite set of observations, H is taken as
constant (actually slightly dependent on the shape of the
precipitation rate histogram), rbar is the average precipitation
rate in mm/mo, S is taken as constant (approximately SQRT(VAR) for
rbar=0), Ni is the number of independent samples in the set of
observations, and the expression in square brackets is a
parameterization of the conditional precipitation rate based on
work with the Goddard Scattering Algorithm, Version 2 (Adler et
al. 1994) and fitting of (1) to the Surface Reference Data Center
analyses (McNab 1995). The "constants" H and S are set for each of
the data sets for which error estimates are required by comparison
of the data set against the Surface Reference Data Center (SRDC)
and GPCC analyses and tropical Pacific atoll gauge data (Morrissey
and Green 1991). The computed value of H actually accounts for
multiplicative errors in Ni and the conditional rainrate
parameterization (the [] term), in addition to H itself. Table 1
shows the numerical values of H and S which are used to estimate
random error for various precipitation estimates.
All absolute error fields have been converted from their original
units of mm/mo to mm/d.
Table 1.
H and S constants
| S |
Technique | (mm/mo) | H
---------------------+---------+-----------------------
| |
SSMI Emission [se] | 30 | 3.25 (55 km images)
| |
SSMI Scattering [ss] | 30 | 4.5 (55 km images)
| |
AGPI [ag] | 20 | 0.6 (2.5 deg images)
| |
Rain Gauge [ga] | 6 | 0.005 (gauges)
Quality and Confidence Estimates:
The "accuracy" of the precipitation products can be broken into
systematic departures from the true answer (bias) and random
fluctuations about the true answer (sampling), as dicussed in
Huffman (1997a). The former are the biggest problem for
climatological averages, since they will not average out. However,
on the monthly time scale the low number of samples tends to
present a more serious problem. That is, for most of the data sets
the sampling is spotty enough that the collection of values over
one month is not yet representative of the true distribution of
precipitation.
Accordingly, the "random error" is assumed to be dominant, and
estimates are computed as discussed for the "absolute error
variable". Note that the rain gauge analysis' random error is just
as real as that of the satellite data, even if somewhat smaller.
Random error cannot be corrected.
The "bias error" is not corrected in the SSM/I emission, SSM/I
scattering, SSM/I composite, and GPI precipitation estimates. In
the AGPI the GPI is adjusted to the large-scale bias of the SSM/I,
which is assumed lower than the GPI's. As noted in the
"satellite-gauge precipitation product" discussion, the
Multi-Satellite product is adjusted to the large-scale bias of the
Gauge analysis before the combination is computed. It continues to
be the case that biases over ocean are not corrected by gauges in
the Multi-Satellite and Satellite-Gauge products.
Four types of "known errors" are contained in part or all of the current
data set, and will be corrected in a future general re-run. They have
been uncovered by visual inspection of the combined data fields over
several years of production, but are considered too minor or insufficiently
understood to provoke an immediate reprocessing.
1. Limit checks on sea ice contamination in the SSM/I emission estimates
have been refined as additional cases are uncovered. The 1997 fields
should be noticably cleaner.
2. The climatological bias correction to the gauge data was capped at
a maximum multiplier of 3, starting in 1997. A few isolated areas in
snowy regions had higher values, particularly in Antarctica and
Siberia.
3. Exact-zero values in marginally snowy land regions (from the SSM/I
scattering field) are probably not reliable, and should simply be
"small."
4. Isolated exact-zero values surrounded by significantly non-zero values
(i.e., >30 mm/mo) in oceanic regions are not reliable. Starting in
1997 they are replaced with the average of the surrounding points (but
none actually occured in the first 6 months of 1997).
Additional Processing
GPCP Version 1a Combined Precipitation Data on 2.5x2.5 degree grid
(array dimension 144x72) has been remapped to 1x1 grid (array
dimension 360x180). The following steps were performed in the
regridding process:
1. Starting with the first latitude band in the original data
set (87.5N to 90N), the first pair of grid cells (total of 5
degrees in longitude) was partitioned into five cells each of
width 1 degree; cells 1 and 2 were assigned the value of the
first 2.5 degree cell, cells 4 and 5 the value of the second
2.5 degree cell, and cell 3 the arithmetic average of the
values of the first and second 2.5 degree cells.
2. In Step 1, if either (but not both) of the original 2.5
degree cells is a fill value, then no average is performed
and cell 3 is assigned the value of the unfilled 2.5 degree
cell. If both of the original cells are fill values, then
cell 3 is likewise assigned this fill value.
3. Steps 1 and 2 were repeated for the remaining 71 pairs of 2.5
grid cells in the original data set
4. Steps 1 through 3 were performed for the remaining 71
latitude bands in the original data set to arrive at a
temporary array of size 360 x 72 (1 degree longitude by 2.5
degrees latitude)
5. The entire procedure above was repeated in the latitudinal
direction using the same grid cell partitioning scheme to
arrive at the final 360 x 180 (1 degree longitude by 1 degree
latitude) array.
6. The regridded data were visually examined to ensure
consistency with the original data.
Scientific Potential of Data
The spatial distribution of precipitation identifies the regions
of maximum latent heat release which is a major driving force of
the atmospheric circulation. The Observed precipitation data need
to be temporarily and spatially integrated (e.g. monthly mean on a
grid area) if it is to be used for the assessment of the earth's
energy, water balance, and monitoring of short-term climate
variability and long-term trends (Hauschild et al., 1994).
Some of the main applications of these precipitation data sets
are:
* Initialization and validation of mesoscale and large-scale
general circulation models ( Hulme, 1992)
* Verification of monthly satellite based precipitation
estimates (Janowiak, 1992)
* Input fields in global hydrological studies ( Lapin, 1994)
* Simulations of the present-day climate and forecasting of
global climate (Krishnamurti et al., 1994)
* Correlation studies, especially during transient events or
periodic events such as El Nino ( Nicholls,1988)
* Agricultutral studies such as detection of the impact of
land-use changes and design of culverts and stream channels
(Rosenzweig and Parry, 1994 )
Validation of Data
An early validation against the Surface Reference Data Center
analysis yields the statistics in Table 2. Overall, the
combination appears to be working as expected.
Table 2
Summary statistics for all cells and months comparing the
SSM/I composite, Multi-satellite, Gauge, and Satellite-gauge products
to the SRDC analysis for July 1987 -- December 1991.
| Bias |Avg of|Diff|| RMS Error
Product | (mm/mo) | (mm/mo) | (mm/mo)
----------------+---------+------------+----------
| | |
SSM/I composite | 4.03 | 60.10 | 88.05
| | |
Multi-satellite | -5.80 | 44.20 | 62.47
| | |
Gauge (GPCC) | 6.77 | 18.85 | 35.11
| | |
Satellite-gauge | 3.70 | 20.29 | 32.98
The "quality index" variable has recently been proposed by Huffman
et al. (1997) and developed in Huffman (1997a) as a way of
comparing the errors computed for different techniques. Absolute
error tends to zero as the average precipitation tends to zero,
while relative error tends to infinity. According to (1), the
dependence is approximately SQRT(rbar) and 1/SQRT(rbar),
respectively. Thus, it is hard to illustrate overall dependence on
sample size with either representation. However, if one inverts
(1) it is possible to get an expression for a number of samples as
a function of precipitation rate and the estimated error variance:
Hg * ( rbarx + Sg) * [ 720 + 268 * SQRT ( rbarx ) ]
Neg = --------------------------------------------------- (2)
VARx
where rbarx and VARx are the precipitation rate and estimated
error variance for technique X, Hg and Sg are the values of H and
S for the gauge analysis, and Neg is the number of "equivalent
gauges," an estimate of the number of gauges that corresponds to
this case. Tests show that Neg is well-behaved over the range of
rbar, largely reflecting the sampling that provided rbarx and
VARx, but also showing differences in the functional form of
absolute error over the range of rbar for different techniques.
Qualitatively, higher Neg denotes more confident answers. Values
above 10 are relatively good. The SSM/I composite estimates tend
to have Neg around 1 or 2, while the AGPI has Neg around 3 or 4.
The rain gauge analysis runs the whole range from 0 to a few grid
boxes in excess of 40.
Contacts
Points of Contact
For information about or assistance in using any DAAC data,
contact
EOS Distributed Active Archive Center (DAAC)
Code 902
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
The original GPCP Combined Precipitation Data Set(on 2.5 by 2.5
degree grid) can be accessed from the Goddard DAAC via this
document GPCP v1a Combined Precipitation Data (Binary data files)
or via FTP
ftp daac.gsfc.nasa.gov
login: anonymous
password: < your internet address >
cd /data/hydrology/precip/gpcp/gpcp_v1a_combined
or contact NCDC Archive
Dr. Alan McNab
World Data Center A (WDC-A)
National Climate Data Center (NCDC)
Rm 514
151 Patton Ave.
Asheville, NC 28801-5001 USA
Internet:amcnab@ncdc.noaa.gov
704-271-4592 (voice)
704-271-4328 (fax)
For algorithm questions related to original data, please contact
the data producers:
Dr. George J. Huffman
Code 912
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Internet: huffman@agnes.gsfc.nasa.gov
301-286-9785 (voice)
301-286-1762 (fax)
and
Dr. Robert Adler
Code 912
NASA Goddard Space Flight Center
Greenbelt, MD 20771 USA
Internet: Adler@agnes.gsfc.nasa.gov
301-286-9086 (voice)
301-286-1762 (fax)
References
Adler, R.F., G.J. Huffman, and P.R. Keehn 1994: Global rain
estimates from microwave-adjusted geosynchronous IR data. Remote
Sens. Rev., 11, 125-152.
Arkin, P. A., R. Joyce, and J. E. Janowiak, 1994: IR techniques:
GOES precipitation index, Remote Sens. Rev., 11, 107-124.
Arkin, P.A., and B. N. Meisner, 1987: The relationship between
large-scale convective rainfall and cold cloud over the Western
Hemisphere during 1982-1984. Mon. Wea. Rev., 115, 51-74.
Chang, A. T., L. S. Chiu, and G. Yang, 1995: Diurnal cycle of
oceanic precipitation from SSM/I data. Mon. Wea. Rev., 123,
3371-3380.
Ferraro, R. R., N. C. Grody, and G. F. Marks, 1994: Effects of
surface conditions on rain identification using the SSM/I. Remote
Sens. Rev., 11, 195-209.
GPCC, 1993. Global area-mean monthly precipitation totals for the
year 1988 (preliminary estimates, derived from rain-gauge
measurements, satellite observations and numerical weather
prediction results). Ed. by WCRP and Deutscher Wetterdienst,
Rep.-No. DWD/K7/WZN-1993/07-1, Offenbach, July 1993.
GPCC, 1992. Monthly precipitation estimates based on gauge
measurements on the continents for the year 1987 (preliminary
results) and future requirements. Ed. by WCRP and Deutscher
Wetterdienst, Rep.-No. DWD/K7 WZN-1992/08-1, Offenbach, August
1992.
Grody, N.C., 1991: Classification of snow cover and precipitation
using the Special Sensor Microwave/Imager (SSM/I). J. Geophys.
Res., 96, 7423-7435.
Hauschild, H., M. Reis, and B. Rudolf, 1994 . Global and
terrestrial precipitation climatologies: An overview and some
intercomparisons. Global Precipitations and Climate Change, M.
Desbois and F. Desalmand, Eds., NATO ASI Series, Vol. 1, No. 26,
Springer-Verlag, 419-434.
Huffman, G.J., 1997a: Estimates of root-mean-square random error
contained in finite sets of estimated precipitation. J. Appl.
Meteor., 36, 1191-1201.
Huffman, G.J., ed., 1997b: The Global Precipitation Climatology
Project monthly mean precipitation data set. WMO/TD No. 808, WMO,
Geneva, Switzerland. 37pp.
Huffman, G.J., R.F. Adler, P.A. Arkin, A. Chang, R. Ferraro, A.
Gruber, J. Janowiak, R.J. Joyce, A. McNab, B. Rudolf, U.
Schneider, and P. Xie, 1997: The Global Precipitation Climatology
Project (GPCP) Combined Precipitation Data Set. Bull. Amer.
Meteor. Soc., 78, 5-20.
Huffman, G.J., R.F. Adler, B. Rudolf, U. Schneider, and P.R.
Keehn, 1995: Global precipitation estimates based on a technique
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